common spatial patterns (CSP)

Common spatial patterns, or CSP, is a classic recipe for finding the best way to mix many electrodes together so that two mental states look as different as possible. Imagine you have dozens of microphones in a room and you want to hear two specific speakers; CSP works out the volume to set on each microphone so that one speaker is loud while the other is quiet, and then a second setting that does the reverse. For the brain, the "speakers" are two states — say, imagining a left-hand versus a right-hand movement.

Concretely, CSP looks at how the signal's strength wobbles over the recording and searches for electrode weightings where that wobble is large for one state and small for the other. Those weightings act as a spatial filter: they collapse a crowd of channels into a few clean "virtual" channels whose energy alone tells the two states apart. A decoder then has a much easier job.

CSP has long been the workhorse for motor-imagery BCIs, where the useful information lives in how much rhythm rises or falls over the motor parts of the brain. It is fast, well understood, and still a strong baseline, though it needs a calibration recording and assumes you are separating just two states at a time.